Bibliographic record
Abstract
Polymer, metallic and ceramic materials in fibrous form are of fundamental importance in materials engineering. Fibrous materials are the basic building blocks for the backbone of most natural and man-made engineering structures, ranging from the skeletal structure of animals to advanced fiber-reinforced composites. Fiber assemblies normally known as textile materials are unique in their combination of strength and toughness, lightweight, flexibility and cost effectiveness. As an essential requirement to fiber and fiber assemblies, mechanical properties are one of the most important properties that need to be characterized and investigated. In this chapter, we will consider the mechanical properties of fiber assemblies from single fiber to fiber assemblies in a hierarchical manner. Structure of hierarchy of textile materials Traditionally fibers are defined as soft materials with a length-to-diameter ratio above 10 3 and a diameter ranging from several to 100 microns. The emergence of nanofibers broadens the span of fibers to the nanoscale world. For engineering applications, fibers are usually employed in different forms such as yarns/ropes, woven textiles and nonwoven textiles. The structure hierarchy of textile materials is shown in Fig. 5.1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".